Abstract
Aiming at the problem of a mobile robot searching human in home environments, a gird model is built and a path planning method based on a modified genetic algorithm and an improved A* algorithm is proposed. First, the grid map is divided into several unit regions using Boustrophedon cellular decomposition. Then, a unit region planning method based on a genetic algorithm is applied to generate a region transition sequence, and an effective strategy to search every region is adjusted according to the robot’s sensors. Meanwhile, the optimal path between two points is generated by an improved A* algorithm, so that the path is much shorter and the number of turns is greatly reduced. Finally, the simulation results verify that this method can provide an optimized path in known home environments effectively, based on that the robot can find human in the shortest possible time.
This work was supported by the National Natural Science Foundation of China under Grant 61328302, and it is also supported by the National Science Foundation (NSF) Grant CISE/IIS 1231671 and CISE/IIS 1427345, the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT1600217).
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Tang, Y., Liu, M., Sheng, W., Zhang, S. (2017). Robot Path Planning for Human Search in Indoor Environments. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_32
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DOI: https://doi.org/10.1007/978-981-10-5230-9_32
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